03650nam a22004935i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001800172072001700190072002300207072001600230100008300246245013200329250001800461264006900479300006700548336002600615337002600641338003600667347002400703490007500727505028500802520148001087650002502567650002902592650002302621650003702644650002902681650004102710700008402751710003402835773002602869776003602895776003602931776003602967830007503003856004603078912001503124999001703139978-3-031-43725-0DE-He21320251030111426.0cr nn 008mamaa240214s2024    sz |    s    |||| 0|eng d  a97830314372509978-3-031-43725-07 a10.1007/978-3-031-43725-02doi 4aTK7867-7867.5 7aTJFC2bicssc 7aTEC0080102bisacsh 7aTJFC2thema1 aGupta, Pramod.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut97098810aEssentials of Python for Artificial Intelligence and Machine Learningh[electronic resource] /cby Pramod Gupta, Anupam Bagchi.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXIX, 509 p. 163 illus., 148 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aSynthesis Lectures on Engineering, Science, and Technology,x2690-03270 aIntroduction -- Statistical Methods and Models -- Python Language Basics -- Introduction to Numpy -- Introduction to Pandas -- Data Manipulation With Pandas -- Data Visualization With Python -- Machine Learning -- Data Pipelines Using Python -- Mlops: Machine Learning Operations.  aThis book introduces the essentials of Python for the emerging fields of Machine Learning (ML) and Artificial Intelligence (AI). The authors explore the use of Python’s advanced module features and apply them in probability, statistical testing, signal processing, financial forecasting, and various other applications. This includes mathematical operations with array data structures, Data Manipulation, Data Cleaning, machine learning, Data pipeline, probability density functions, interpolation, visualization, and other high-performance benefits using the core scientific packages NumPy, Pandas, SciPy, Sklearn/Scikit learn and Matplotlib. Readers will gain a deep understanding with problem-solving experience on these powerful platforms when dealing with engineering and scientific problems related to Machine Learning and Artificial Intelligence. Several examples of real problems using these techniques are provided along with examples. The authors also focus on the best practices in the industry on using Python for AI and ML. Deployment on a cloud infrastructure is described in detail (with code) to emphasize real scenarios. Includes several real examples of how to write and deploy code, including on a cloud infrastructure Provides single-source on Python for machine learning and artificial intelligence, from basics to real implementation Includes sufficient coverage of Python libraries, frameworks, and tools to develop complex data science applications. 0aElectronic circuits. 0aMachine learning.955059 0aSignal processing.14aElectronic Circuits and Systems.24aMachine Learning.95505924aSignal, Speech and Image Processing.1 aBagchi, Anupam.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9709892 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303143724308iPrinted edition:z978303143726708iPrinted edition:z9783031437274 0aSynthesis Lectures on Engineering, Science, and Technology,x2690-032740uhttps://doi.org/10.1007/978-3-031-43725-0  aZDB-2-SXSC  c57620d57620